A. Naseri Jahfari | TU Delft Repository

Improving Remote Cardiovascular Care with Wearable Data

Algorithms, Study Design, and Subject-Specific Adaptation

Doctoral thesis (2026) - A. Naseri Jahfari, M.J.T. Reinders, D.M.J. Tax, Ivo van der Bilt

Cardiovascular diseases remain the leading cause of death worldwide, yet early detection and continuous monitoring remain challenging outside clinical settings. This dissertation is motivated by the growing potential of remote health monitoring to address this gap—specifically, the use of consumer-grade smartwatches to track cardiovascular health through physiological signals. Although consumer-grade wearables are traditionally merely used as fitness-oriented or recreational, this work investigates the clinical applicability of smartwatch-derived signals for disease monitoring in real-world, non-clinical environments. By enabling scalable, data-driven detection of cardiovascular conditions in everyday settings, such a system has the potential to reduce the burden on physicians, provide patients with continuous insights, and alleviate pressure on healthcare systems through earlier intervention and more personalized care.

By assessing how far wearable-based research has progressed toward operational deployment and identify critical shortcomings in real-world utility and generalizability, we confront several major challenges intrinsic to this domain: the medical interpretability of noisy consumer-grade signals, high inter-subject variability, and the inherent complexity of timeseries data that varies with context (e.g., day/night cycles, physical activity).

Our solution strategy is grounded in machine learning techniques that aim to learn robust, transferable representations of physiological data. In particular, we explore contrastive learning, weak supervision, and morphological modeling—such as acceleration-deceleration curve analysis— as tools to extract clinically relevant patterns. These methods are evaluated across both publicly available and proprietary datasets to ensure applicability to diverse populations.

By addressing these challenges, this dissertation advances the case for smartwatches as viable tools for longitudinal, data-efficient cardiovascular monitoring, contributing to a future in which early detection of conditions like atrial fibrillation and heart failure is feasible at scale in everyday settings.
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Cardiovascular diseases remain the leading cause of death worldwide, yet early detection and continuous monitoring remain challenging outside clinical settings. This dissertation is motivated by the growing potential of remote health monitoring to address this gap—specifically, the use of consumer-grade smartwatches to track cardiovascular health through physiological signals. Although consumer-grade wearables are traditionally merely used as fitness-oriented or recreational, this work investigates the clinical applicability of smartwatch-derived signals for disease monitoring in real-world, non-clinical environments. By enabling scalable, data-driven detection of cardiovascular conditions in everyday settings, such a system has the potential to reduce the burden on physicians, provide patients with continuous insights, and alleviate pressure on healthcare systems through earlier intervention and more personalized care.

By assessing how far wearable-based research has progressed toward operational deployment and identify critical shortcomings in real-world utility and generalizability, we confront several major challenges intrinsic to this domain: the medical interpretability of noisy consumer-grade signals, high inter-subject variability, and the inherent complexity of timeseries data that varies with context (e.g., day/night cycles, physical activity).

Our solution strategy is grounded in machine learning techniques that aim to learn robust, transferable representations of physiological data. In particular, we explore contrastive learning, weak supervision, and morphological modeling—such as acceleration-deceleration curve analysis— as tools to extract clinically relevant patterns. These methods are evaluated across both publicly available and proprietary datasets to ensure applicability to diverse populations.

By addressing these challenges, this dissertation advances the case for smartwatches as viable tools for longitudinal, data-efficient cardiovascular monitoring, contributing to a future in which early detection of conditions like atrial fibrillation and heart failure is feasible at scale in everyday settings.

Tackling inter-subject variability in smartwatch data using factorization models

Journal article (2025) - Arman Naseri, David M.J. Tax, Ivo van der Bilt, Marcel Reinders

Smartwatches enable longitudinal and continuous data acquisition. This has the potential to remotely monitor (changes) of the health of users. However, differences among subjects (inter-subject variability) limit a model to generalize to unseen subjects. This study focused on binary classification tasks using heart rate and step counter from smartwatches, including night/day and inactive/active classification, as well as sleep and SpO2-related (oxygen saturation) tasks. To address inter-subject variability, we explored different transforming and normalization regimes for time series including per-subject and population-based strategies. We propose a modified factorized autoencoder, which separates the data into two latent spaces capturing class-specific and subject-specific information. Our proposed generalized factorized autoencoder and triplet factorized autoencoder improved classification accuracy over the baseline from 74.8 (± 10.5) to 83.1 (± 5.1) and 83.4 (± 5.3), respectively, for night/day classification, gains for inactive/active classification were modest, improving from 84.3 (± 9.4) to 86.9 (± 4.4) and 86.6 (± 4.3), respectively. Our study highlights challenges of handling inter-subject variability in smartwatch data and how factorization models can be used to enable more robust and personalized health monitoring solutions for diverse populations. ...

Heart disease detection using an acceleration-deceleration curve-based neural network with consumer-grade smartwatch data

Journal article (2024) - Arman Naseri , David M.J. Tax, Marcel Reinders, Ivo van der Bilt

Cardiovascular disease (CVD) is the most important cause of morbidity and mortality worldwide. Early detection, prevention or even prediction is of pivotal importance to reduce the burden of cardiovascular disease and its associated costs. Low cost, consumer-grade smartwatches have the potential to revolutionize cardiovascular medicine by enabling continuous monitoring of heart rate and activity. When combined with machine learning(ML), the resulting large amounts of time series data hold the potential of detection, or exclusion of CVD. However, analyzing such large datasets is challenging due to the sparse presence of informative segments. Efficient selection of these segments is essential for developing predictive models for clinical deployment. The objective of this paper was to investigate the potential of an acceleration-deceleration curvebased ML model as a novel clinical indicator for the detection of cardiovascular diseases. We used data from the ME-TIME study; 42 participants from which 21 have a cardiovascular disease and 21 are health controls. Data from each subject was normalized to decrease inter-subject variability. A neural network model aggregated predictions per week. We showed that per-subject normalization by the peak value of curves during inactivity, aggregation of model predictions over a week, and using a contrastive loss, resulted in a predictive model with 99 % ± 3 % specificity and 40 % ± 49 % sensitivity on the development set, and 100 % specificity with 67 % ± 47 % sensitivity on the test set. Acceleration-deceleration curves are effective patterns for ruling out the presence of cardiovascular disease, but caution must be taken to properly pre-process the curves and carefully choosing a model that reduces the variability in the extracted curves. ...

Data-efficient machine learning methods in the ME-TIME study

Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables

Journal article (2023) - Arman Naseri, David Tax, Pim van der Harst, Marcel Reinders, Ivo van der Bilt

Background: Smartwatches enable continuous and noninvasive time series monitoring of cardiovascular biomarkers like heart rate (from photoplethysmograms), step counter, skin temperature, et cetera; as such, they have promise in assisting in early detection and prevention of cardiovascular disease. Although these biomarkers may not be directly useful to physicians, a machine learning (ML) model could find clinically relevant patterns. Unfortunately, ML models typically need supervised (ie, annotated) data, and labeling of large amounts of continuous data is very labor intensive. Therefore, ML methods that are data efficient, ie, needing a low number of labels, are required to detect potential clinical value in patterns found in wearable data. Objective: The primary study objective of the ME-TIME (Machine Learning Enabled Time Series Analysis in Medicine) study is to design an ML model that can detect atrial fibrillation (AF) and heart failure (HF) from wearable data in a data-efficient manner. To achieve this, self-supervised and weakly supervised learning techniques are used. Methods: Two hundred subjects (100 reference, 50 AF, and 50 HF) are being invited to participate in wearing a Fitbit fitness tracker for 3 months. Interested volunteers are sent a questionnaire to determine their health, in particular cardiovascular health. Volunteers without any (history of) serious illness are assigned to the reference group. Participants with AF and HF are recruited in the Haga teaching hospital in The Hague, The Netherlands. Results: Enrollment commenced on May 1, 2022, and as of the time of this report, 62 subjects have been included in the study. Preliminary analysis of the data reveals significant inter-subject variability. Notably, we identified heart rate recovery curves and time-delayed correlations between heart rate and step count as potential strong indicators for heart disease. Conclusion: Using self-supervised and multiple-instance learning techniques, we hypothesize that patterns specific to AF and HF can be found in continuous data obtained from smartwatches. ...

Background: Smartwatches enable continuous and noninvasive time series monitoring of cardiovascular biomarkers like heart rate (from photoplethysmograms), step counter, skin temperature, et cetera; as such, they have promise in assisting in early detection and prevention of cardiovascular disease. Although these biomarkers may not be directly useful to physicians, a machine learning (ML) model could find clinically relevant patterns. Unfortunately, ML models typically need supervised (ie, annotated) data, and labeling of large amounts of continuous data is very labor intensive. Therefore, ML methods that are data efficient, ie, needing a low number of labels, are required to detect potential clinical value in patterns found in wearable data. Objective: The primary study objective of the ME-TIME (Machine Learning Enabled Time Series Analysis in Medicine) study is to design an ML model that can detect atrial fibrillation (AF) and heart failure (HF) from wearable data in a data-efficient manner. To achieve this, self-supervised and weakly supervised learning techniques are used. Methods: Two hundred subjects (100 reference, 50 AF, and 50 HF) are being invited to participate in wearing a Fitbit fitness tracker for 3 months. Interested volunteers are sent a questionnaire to determine their health, in particular cardiovascular health. Volunteers without any (history of) serious illness are assigned to the reference group. Participants with AF and HF are recruited in the Haga teaching hospital in The Hague, The Netherlands. Results: Enrollment commenced on May 1, 2022, and as of the time of this report, 62 subjects have been included in the study. Preliminary analysis of the data reveals significant inter-subject variability. Notably, we identified heart rate recovery curves and time-delayed correlations between heart rate and step count as potential strong indicators for heart disease. Conclusion: Using self-supervised and multiple-instance learning techniques, we hypothesize that patterns specific to AF and HF can be found in continuous data obtained from smartwatches.

Machine Learning for Cardiovascular Outcomes from Wearable Data

Systematic Review from a Technology Readiness Level Point of View

Review (2022) - Arman Naseri Jahfari, David Tax, Marcel Reinders, Ivo van der Bilt

Background: Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. Objective: This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. Methods: We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as “wearables,” “machine learning,” and “cardiovascular disease.” Methodologies were categorized and analyzed according to machine learning-based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). Results: After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. Conclusions: Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation. ...

Background: Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. Objective: This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. Methods: We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as “wearables,” “machine learning,” and “cardiovascular disease.” Methodologies were categorized and analyzed according to machine learning-based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). Results: After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. Conclusions: Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.

Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings

Conference paper (2020) - Katja Geertruida Schmahl, Tom Julian Viering, Stavros Makrodimitris, Arman Naseri Jahfari, David Tax, Marco Loog

Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain stereotypical gender biases. As a result, such unwanted biases will typically also be present in word embeddings derived from such corpora and downstream applications in the field of natural language processing (NLP). To minimize the effect of gender bias in these settings, more insight is needed when it comes to where and how biases manifest themselves in the text corpora employed. This paper contributes by showing how gender bias in word embeddings from Wikipedia has developed over time. Quantifying the gender bias over time shows that art related words have become more female biased. Family and science words have stereotypical biases towards respectively female and male words. These biases seem to have decreased since 2006, but these changes are not more extreme than those seen in random sets of words. Career related words are more strongly associated with male than with female, this difference has only become smaller in recently written articles. These developments provide additional understanding of what can be done to make Wikipedia more gender neutral and how important time of writing can be when considering biases in word embeddings trained from Wikipedia or from other text corpora. ...